The results of this study show that the prediction method used with the help of neural networks is a very conducive model for stock price prediction.Gnanendra, MJana, Jesna
In this research, we study the problem of Chinese stock market forecasting using traditional Neural Network methods, including Deep Feedforward Network, Convolution Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM) and we have also integrate with the Bi-direction techn...
Convolutional Neural NetworkMultivariate Time SeriesPrediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market HypothesiMehtab, SidraSen, JaydipSocial Science Electronic Publishing...
Stock Exchange Prediction using neural networks has been an interesting research problem whereby many researchers have developed a keen interest in prediction of future values and trends. Little research has been done to apply and improve prediction models based on newer and impactful variables to show...
Prediction of stock market returns is an important issue in finance. Artificial neural networks have been used in stock market prediction during the last decade. Studies were performed for the prediction of stock index values as well as daily direction of change in the index. In some applications...
This paper also provides the effect of various topological parameters on the accuracy and training time of neural networks. A topology of neural network is proposed for the prediction of Indian stock market index S&P CNX NIFTY.doi:10.1080/02564602.2006.11657936Rihani, V...
A novel neural network ensemble model is proposed for stock market prediction.First of all,the original data of time series are reconstructed for reduction the noise and extraction the tendency by Singular Spectrum Analysis(SSA).Secondly,C-C algorithm are adopted to confirm the best delay time and...
White H (1988) Economic prediction using neural networks: the case of IBM daily stock returns 451–458 Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175 Article Google Scholar Vanstone B, Finnie G (2009) An empirical methodology...
B. LSTM for Time series Prediction LSTM神经网络的输入是序列,它们是CNN模型的输出。每个序列分为多个元素。在每个时间步长,一个元素用作输入。如图3所示,空白圆圈代表状态,灰色圆圈代表输入。如果按照时间步长展开LSTM,则可以将LSTM表示为网络,如图3右侧所示。每个时间步长的输出和输入表示为oi和xi。
A general stock prediction model based on neural networks New 20230522 经过长时间的训练,分析和学习,我深深感觉到单纯使用lstm和transformer进行价格的预测是相当的困难。我下面的更新方向将向三个方向进行:一是开发一种新的模型以更加适配金融预测的特点; 二是继续完成NLP方向的情感分析,做到分析大众和专业机构的恐...